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A data steward is a role that ensures that data governance processes are followed and that guidelines are enforced, and recommends improvements to data governance processes. Data governance involves the coordination of people, processes, and information technology necessary to ensure consistent and proper management of an organization's data ...
The Data Governance Act (DGA) is a regulation by the European Union that aims to create a framework which will facilitate data-sharing. [ 1 ] [ 2 ] The proposal was first announced within the 2020 European strategy for data and was officially presented by Margrethe Vestager in 25 November 2020. [ 3 ]
It encompasses topics such as data architecture, security, quality, modelling, governance, [9] big data, data science, and more. [10] The DMBok includes the DAMA Data Wheel. The infographic represents the core data management practices having data governance as at its centre. The surrounding segments each represent a different aspect of data ...
Information governance goes beyond retention and disposition to include privacy, access controls, and other compliance issues. In electronic discovery, or e-discovery, relevant data in the form of electronically stored information is searched for by attorneys and placed on legal hold. IG includes consideration of how this data is held and ...
However, data has to be of high quality to be used as a business asset for creating a competitive advantage. Therefore, data governance is a critical element of data collection and analysis since it determines the quality of data while integrity constraints guarantee the reliability of information collected from data sources.
The Open Group Architecture Framework (TOGAF) is the most used framework for enterprise architecture as of 2020 [2] that provides an approach for designing, planning, implementing, and governing an enterprise information technology architecture. [3] TOGAF is a high-level approach to design.
The Data Owner is responsible for the requirements for data definition, data quality, data security, etc. as well as for compliance with data governance and data management procedures. The Data Owner should also be funding improvement projects in case of deviations from the requirements.
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
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